metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_8_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-sw-1hr-v1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_8_0
          type: common_voice_8_0
          config: sw
          split: test
          args: sw
        metrics:
          - name: Wer
            type: wer
            value: 0.5901667526216263

wav2vec2-large-xls-r-300m-sw-1hr-v1

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_8_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.8004
Wer: 0.5902
Cer: 0.1498

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 60
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
10.7706	4.6512	100	5.0262	1.0	1.0
3.7038	9.3023	200	3.2132	1.0	1.0
2.9571	13.9535	300	2.8597	1.0	1.0
2.7859	18.6047	400	2.6007	1.0	0.7810
1.2103	23.2558	500	0.8662	0.6976	0.1859
0.3075	27.9070	600	0.7534	0.6533	0.1695
0.1911	32.5581	700	0.7585	0.6282	0.1607
0.1482	37.2093	800	0.8062	0.6340	0.1667
0.1241	41.8605	900	0.7999	0.6190	0.1605
0.1085	46.5116	1000	0.8105	0.6001	0.1524
0.0935	51.1628	1100	0.7972	0.5914	0.1502
0.0833	55.8140	1200	0.7978	0.5931	0.1505

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1